Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Random Data: Analysis and Measurement Procedures
Random Data: Analysis and Measurement Procedures
Localization of the complex spectrum: the S transform
IEEE Transactions on Signal Processing
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Auto Regressive Integrated Moving Average (ARIMA) is a broad class of time series models, and it has been achieved using the statistical differencing approach. It is normally being performed using the computational method. Thus, it is useful to choose the suitable model from a possibly large selection of the available ARIMA formulations. The ARIMA approach was then analysed with the presence of stationary behaviour in a nonstationary data. For the purpose of the random data analysis, a nonstationary data that exhibiting a random behaviour was used. This random data was measured in the unit of microstrain on the lower suspension arm or a car travelling on a country road surface. With this engineering unit, hence, the data is known as a variable amplitude fatigue loading. Experimentally, the data was collected for 225 seconds at the sampling rate of 200 Hz, which gave 45,000 discrete data points. Using the computational analysis by means of statistical software package, the ARIMA parameters were estimated by the application of the data smoothing technique in order to reduce the random variation of the fatigue data. Therefore, the significant ARIMA parameters were established and being applied in the study of the variation in nonstationary data. For this paper, finally, it is suggested that the ARIMA method provided a good platform to analyse fatigue random data, especially in the scope of the durability research.